Ipsen Banke (riselyre94)

Millions of somatic mutations have recently been discovered in cancer genomes. These mutations in cancer genomes occur due to internal and external mutagenesis forces. Decoding the mutational processes by examining their unique patterns has successfully revealed many known and novel signatures from whole exome data, but many still remain undiscovered. Here, we developed a deep learning approach, DeepMS, to decompose mutational signatures using 52,671,908 somatic mutations from 2780 highly curated cancer genomes with whole genome sequencing (WGS) in 37 cancer types/subtypes. With rigorous model training and comparison, we characterized 54 signatures for single base substitutions (SBSs), 11 for doublet base substitutions (DBSs) and 16 for small insertions and deletions (Indels). Compared to the previous methods, DeepMS could discover 37 SBS, 5 DBS, and 9 Indel new signatures, many of which represent associations with DNA mismatch or base excision repair and cisplatin therapy mechanisms. We further developed a regression-based model to estimate the correlation between signatures and clinical and demographical phenotypes. The first deep learning model DeepMS on WGS somatic mutational profiles enable us identify more comprehensive context-based mutational signatures than traditional NMF approaches. CK666 Our work substantially expands the landscape of the naturally occurring mutational signatures in cancer genomes, and provides new insights into cancer biology.The current paradigm holds that the inhibition of Rho guanosine nucleotide exchange factors (GEFs), the enzymes that stimulate Rho GTPases, can be a valuable therapeutic strategy to treat Rho-dependent tumors. However, formal validation of this idea using in vivo models is still missing. In this context, it is worth remembering that many Rho GEFs can mediate both catalysis-dependent and independent responses, thus raising the possibility that the inhibition of their catalytic activities might not be sufficient per se to block tumorigenic processes. On the other hand, the inhibition of these enzymes can trigger collateral side effects that could preclude the practical implementation of anti-GEF therapies. To address those issues, we have generated mouse models to mimic the effect of the systemic application of an inhibitor for the catalytic activity of the Rho GEF Vav2 at the organismal level. Our results indicate that lowering the catalytic activity of Vav2 below specific thresholds is sufficient to block skin tumor initiation, promotion, and progression. They also reveal that the negative side effects typically induced by the loss of Vav2 can be bypassed depending on the overall level of Vav2 inhibition achieved in vivo. These data underscore the pros and cons of anti-Rho GEF therapies for cancer treatment. They also support the idea that Vav2 could represent a viable drug target.Most viral pathogens in humans have animal origins and arose through cross-species transmission. Over the past 50 years, several viruses, including Ebola virus, Marburg virus, Nipah virus, Hendra virus, severe acute respiratory syndrome coronavirus (SARS-CoV), Middle East respiratory coronavirus (MERS-CoV) and SARS-CoV-2, have been linked back to various bat species. Despite decades of research into bats and the pathogens they carry, the fields of bat virus ecology and molecular biology are still nascent, with many questions largely unexplored, thus hindering our ability to anticipate and prepare for the next viral outbreak. In this Review, we discuss the latest advancements and understanding of bat-borne viruses, reflecting on current knowledge gaps and outlining the potential routes for future research as well as for outbreak response and prevention efforts.Starting a research group in a developing country can be economically, intellectually and personally challenging, but funding and other opportunities may be broader than they may seem from afar.Developments in techniques for identification